A review of automated and data-driven approaches for pathway determination and reaction monitoring in complex chemical systems

Puliyanda, Anjana; Srinivasan, Karthik; Sivaramakrishnan, Kaushik; Prasad, Vinay

doi:10.1016/j.dche.2021.100009

Cited by 11 publications

(14 citation statements)

References 67 publications

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“…However, in the context of online reaction monitoring, sophisticated spectroscopic curve resolution algorithms can be used to project real-time spectra onto the temporal data collection mode and the spectroscopic channels, which in accordance with Beer’s law, gain interpretability as the pseudo-component concentrations and pseudo-component spectra, respectively. The kinetics of the underlying chemical transformations can then be assessed from the temporal concentration projections to further facilitate control and optimization . The automated mapping of the hypothesized reaction networks to domain knowledge-based real chemistries can aid the future development of an interpretable end-to-end pipeline to identify species, reactions, and kinetics from spectroscopic data.…”

Section: Discussionmentioning

confidence: 99%

Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass

Sattari

Srinivasan

Puliyanda

et al. 2023

Ind. Eng. Chem. Res.

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In this work, we present and validate a methodology for generating reaction networks from spectroscopic data using data-driven methods by applying it to the hydrothermal liquefaction (HTL) of Monterrey pine biomass and its constituents, viz., cellulose and lignin. This work is presented as a step toward automated inference of chemistry of the hydrothermal liquefaction process, thus limiting the need for human expertise. Bayesian hierarchical clustering of spectra and selfmodeling multivariate spectral curve resolution are used to generate groups of chemically similar species, the reaction networks among which have been developed using Bayesian networks. Fourier transform infrared spectroscopy and proton nuclear magnetic resonance spectroscopy-based measurements are used as input data. The data-driven reaction network includes pathways representing decomposition of the biomass components, large molecule hydrolysis, and reformation of produced molecules and is consistent with the literature. Furthermore, the comparison of the networks generated for biomass and its components (levoglucosan, representing cellulose, and 2-phenoxy-ethyl benzene, representing lignin) reveals the relationship between the biomass HTL reaction network and the reaction networks of the components. The data-driven approach provides a diagnostic tool to identify the most probable reaction chemistry for complex biomass feedstocks and can be used for process understanding, design, and control.

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Section: Discussionmentioning

confidence: 99%

Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass

Sattari

Srinivasan

Puliyanda

et al. 2023

Ind. Eng. Chem. Res.

Self Cite

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“…Contemporary computational chemistry has reached a stage at which massive exploration into chemical reaction space with an unprecedented resolution with respect to the number of potentially relevant molecular structures is becoming a realistic task. Various algorithmic advances have shown that extensive structural screenings can nowadays be automated and carried out using modern computational chemistry protocols. − Automated computational strategies for predicting multistep reaction mechanisms for complex chemical processes, such as pyrolysis, combustion, or catalytic transformations, offer substantial advantages over the conventional strategy largely based on the expert-guided exploration of selected and restricted number of mechanistic alternatives.…”

Section: Introductionmentioning

confidence: 99%

“…A conventional workflow in applied computational catalysis studies approaches this task via manual structural explorations, which rely largely on the expert knowledge and a substantial amount of chemical intuition, thus limiting the study to the expected reactivity domains. The last decade has seen a rapid development of various computational approaches to automate the exploration and discovery of complex chemical reaction networks, targeting the reconstruction of a complete atomistic representation of the mechanism of a chemical conversion process. − Strategies for the accelerated exploration of reaction networks can vary substantially in the computational costs as well as the comprehensiveness and accuracy of the chemical reaction network that they produce. , …”

Section: Introductionmentioning

confidence: 99%

ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis

Hashemi

Bougueroua

Gaigeot

et al. 2022

J. Chem. Theory Comput.

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Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed, which puts forward a network formalism of homogeneous catalysis reactions and utilizes a network analysis tool for mechanistic studies. The method can be used for analyzing trajectories with single and multiple catalytic species and can provide unique conformers of catalysts including multinuclear catalyst clusters along with other catalytic mixture components. The presented three-step approach has the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvents). It is not limited to predefined chemical rules, does not require prealignment of reaction mixture components consistent with a reaction coordinate, and is not agnostic to the chemical nature of transformations. Conformer exploration, reactive event identification, and reaction network analysis are the main steps taken for identifying the pathways in catalytic systems given the starting precatalytic reaction mixture as the input. Such a methodology allows us to efficiently explore catalytic systems in realistic conditions for either previously observed or completely unknown reactive events in the context of a network representing different intermediates. Our workflow for the catalytic reaction space exploration exclusively focuses on the identification of thermodynamically feasible conversion channels, representative of the (secondary) catalyst deactivation or inhibition paths, which are usually most difficult to anticipate based solely on expert chemical knowledge. Thus, the expert bias is sought to be removed at all steps, and the chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring the reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events.

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“…Various algorithmic advances have shown that extensive structural screenings can nowadays be automated and carried out using modern computational chemistry protocols. [1][2][3][4][5] Automated computational strategies for predicting multi-step reaction mechanisms for complex chemical processes such as pyrolysis, combustion or catalytic transformations offer substantial advantages over the conventional strategy largely based on the expert-guided exploration of selected and restricted number of mechanistic alternatives.…”

Section: Introductionmentioning

confidence: 99%

“…The last decade has seen a rapid development of various computational approaches to automate the exploration and discovery of complex chemical reaction networks targeting the reconstruction of a complete atomistic representation of the mechanism of a chemical conversion process. [1][2][3][4][5] Strategies for the accelerated exploration of reaction networks can vary substantially in the computational costs as well as the comprehensiveness and accuracy of the chemical reaction network that they produce. 19,20 For example, the Global Reaction Route Mapping (GRRM) approach introduced by Maeda et al, 21 in which starting from a given "reactant" configuration, the PES is explored to discover new transition states and intermediates forming the reaction network.…”

Section: Introductionmentioning

confidence: 99%

ReNeGate: Reaction Network Graph Theoretical tool for automated mechanistic studies in computational homogeneous catalysis

Hashemi

Bougueroua

Gaigeot

et al. 2022

Preprint

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Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is a challenge for modern computational protocols. In order to remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed to provide mechanistic analysis in catalytic systems. The primary advantage of the presented three-step approach over the existing automated pathways generation methods is the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvent). It is not limited to pre-defined chemical rules, does not require pre-alignment of reaction mixture components consistent with a reaction coordinate and is not agnostic to the chemical nature of transformations. Conformer exploration, Reactive event identification and Reaction network analysis are the main steps taken for understanding the underlying mechanistic pathways in catalytic mixtures given the reaction mixture as the input. Such a methodology allows to comprehensively explore the catalytic systems in realistic conditions for either previously observed or completely unknown reactive events. The expert bias is sought to be removed in either of the steps and chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events.

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A review of automated and data-driven approaches for pathway determination and reaction monitoring in complex chemical systems

Cited by 11 publications

References 67 publications

Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass

Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass

ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis

ReNeGate: Reaction Network Graph Theoretical tool for automated mechanistic studies in computational homogeneous catalysis

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